Abstract
Indexter is a plan-based model of narrative that incorporates cognitive scientific theories about the salience of narrative events. A pair of Indexter events can share up to five indices with one another: protagonist, time, space, causality, and intentionality. The pairwise event salience hypothesis states that when a past event shares one or more of these indices with the most recently narrated event, that past event is more salient, or easier to recall, than an event which shares none of them. In this study we demonstrate that we can predict user choices based on the salience of past events. Specifically, we investigate the hypothesis that when users are given a choice between two events in an interactive narrative, they are more likely to choose the one which makes the previous events in the story more salient according to this theory.
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Notes
- 1.
Here we use the one protagonist per event (as opposed to one per story) definition discussed by Cardona-Rivera et al. [3].
References
Bae, B.C., Young, R.M.: A computational model of narrative generation for surprise arousal. IEEE Trans. Comput. Intell. Artif. Intell. Games 6(2), 131–143 (2014)
Bal, M.: Narratology: introduction to the theory of narrative. University of Toronto Press (1997). http://books.google.com/books?isbn=1442692227
Cardona-Rivera, R.E., Cassell, B.A., Ware, S.G., Young, R.M.: Indexter: a computational model of the event-indexing situation model for characterizing narratives. In: Proceedings of the 3rd Workshop on Computational Models of Narrative, pp. 34–43 (2012). (Awarded Best Student Paper on a Cognitive Science Topic)
Cardona-Rivera, R.E., Robertson, J., Ware, S.G., Harrison, B., Roberts, D.L., Young, R.M.: Foreseeing meaningful choices. In: Proceedings of the 10th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 9–15 (2014)
Cardona-Rivera, R.E., Young, R.M.: A knowledge representation that models memory in narrative comprehension. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence - Student Abstracts Track, pp. 3098–3099 (2014). https://liquidnarrative.csc.ncsu.edu/wp-content/uploads/sites/15/2015/11/cardona-rivera2014knowledge.pdf
Cheong, Y.-G., Young, R.M.: Narrative generation for suspense: modeling and evaluation. In: Spierling, U., Szilas, N. (eds.) ICIDS 2008. LNCS, vol. 5334, pp. 144–155. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89454-4_21
El-Nasr, M.S., Vasilakos, A.V., Rao, C., Zupko, J.A.: Dynamic intelligent lighting for directing visual attention in interactive 3-D scenes. IEEE Trans. Comput. Intell. AI Games 1(2), 145–153 (2009). http://dx.doi.org/10.1109/TCIAIG.2009.2024532
Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3), 189–208 (1972)
Fleiss, J., Levin, B., Paik, M.: Statistical Methods for Rates and Proportions. Wiley Series in Probability and Statistics, Wiley (2013). http://books.google.co.in/books?id=9Vef07a8GeAC
Jhala, A., Young, R.M.: Cinematic visual discourse: representation, generation, and evaluation. IEEE Trans. Comput. Intell. Artif. Intell. Games 2(2), 69–81 (2010)
Kives, C., Ware, S.G., Baker, L.J.: Evaluating the pairwise event salience hypothesis in Indexter. In: Proceedings of the 11th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 30–36 (2015)
Magliano, J.P., Miller, J., Zwaan, R.A.: Indexing space and time in film understanding. Appl. Cogn. Psychol. 15(5), 533–545 (2001)
Riedl, M.O., Young, R.M.: Narrative planning: balancing plot and character. J. Artif. Intell. Res. 39(1), 217–268 (2010)
Roberts, D.L., Isbell, C.L.: Lessons on using computationally generated influence for shaping narrative experiences. IEEE Trans. Comput. Intell. AI Games 6(2), 188–202 (2014)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010)
Trabasso, T., Sperry, L.L.: Causal relatedness and importance of story events. J. Mem. Lang. 24(5), 595–611 (1985)
Trabasso, T., Van Den Broek, P.: Causal thinking and the representation of narrative events. J. Mem. Lang. 24(5), 612–630 (1985)
Young, R.M., Cardona-Rivera, R.E.: Approaching a player model of game story comprehension through affordance in interactive narrative. In: Proceedings of the 4th Workshop on Intelligent Narrative Technologies, pp. 123–130 (2011)
Young, R.M., Ware, S.G., Cassell, B.A., Robertson, J.: Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and interactivity in narratives. Sprache und Datenverarbeitung, Special Issue Formal Comput. Models Narrative 37(1–2), 41–64 (2013)
Zacks, J.M., Speer, N.K., Reynolds, J.R.: Segmentation in reading and film comprehension. J. Exp. Psychol. Gen. 138(2), 307 (2009)
Zwaan, R.A., Radvansky, G.A.: Situation models in language comprehension and memory. Psychol. Bull. 123(2), 162 (1998)
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Farrell, R., Ware, S.G. (2016). Predicting User Choices in Interactive Narratives Using Indexter’s Pairwise Event Salience Hypothesis. In: Nack, F., Gordon, A. (eds) Interactive Storytelling. ICIDS 2016. Lecture Notes in Computer Science(), vol 10045. Springer, Cham. https://doi.org/10.1007/978-3-319-48279-8_13
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